Prediction of Traffics Next State by Classification Data Mining ...

Predicting the Next State of Traffic by Data Mining Classification Techniques S. Mehdi Hashemi Mehrdad Almasi Roozbeh Ebrazi Intelligent Transportation System Research Institute (ITSRI) Amirkabir University of Technology, Tehran, Iran

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Outline 1. Introduction

Prediction important role in ITS Traffic state predictions classification Some popular prediction models Short-term level of service (LOS) prediction and possible application

2. Proposed approach

Supervised classification data mining algorithms Classification tree, Random Forest and Naïve Bayesian

3. Results

Real-world traffic data set of Hakim highway in Tehran, Iran

4. Concluding comments

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Introduction Prediction has an important role in ITS

Short term predictions could either be used directly by traffic experts to take relevant actions or could be injected as inputs to proactive approaches. These approaches include

Route guidance (RG), Dynamic congestion pricing, Variable Speed Limits (VSL), Ramp Metering (RM)

long-term predictions can be used for transportation planning applications

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Introduction Traffic state predictions classification

There exists various categories based on diverse classification standards:

Single link or transportation network Univariate or multivariate Physical models or mathematical methodologies

Applying statistical methodologies , prediction approaches can be grouped into two main categories:

Parametric techniques Non-parametric techniques

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Introduction Traffic state predictions classification Parametric techniques:

These techniques try to detect a function between the past information and the predicted state. However, these methods are typically sensitive to errors and data quality. Filtering techniques, Linear and Nonlinear regression, Autoregressive Moving Average Family (ARMA/ARIMA/SARIMA)

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Introduction Traffic state predictions classification Non-parametric techniques:

Nonparametric statistical regression can be regarded as a dynamic clustering model that relies on the relationship between dependent and independent traffic variables. It attempts to identify past information that are similar to the state at prediction time, which leads to easily implemented nature(Zhang &Liu 2009). Such techniques can generally handle imprecise data and as a result of this capability, usually in dealing with the nondeterministic, complex and nonlinear system perform well.

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Introduction Short-term level of service (LOS) prediction and possible application:

Several approaches (Chrobok et al., 2004; Lili, Peeta et al., 2012; Chen et al., 2012; Smith et al., 2002) ,usually by utilizing neural networks or unsupervised algorithms, consider prediction of flow, travel time, etc. Since traffic patterns and driver behavior changes in different times and traffic stats, we motivated to predict short-term level of service (LOS).

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Possible advantages of this prediction in terms of computation, model accuracy:

producing simple and easy handling if-rules that can be used in expert systems, finding performance quality of road facilities characteristics and proposing optimal speed for that section, detecting failure in the specified state increased accuracy of macroscopic simulation models e.g METANET Model (Papageorgiou et al., 2010; Hegyi & Schutter, 2003; Ghods et al., 2010) by setting boundary conditions and calibration parameters of these models appropriately and proportional to predicted LOS for the next state of traffic.

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Possible advantages this prediction in terms of computation, model accuracy:

This kind of simulation model has broad usage in ITS proactive controller. However simple produced rule itself has the capability to be used in decision tree of this kind traffic controller system (e.g. Variable Speed Limit system (Allaby et al., 2006)).

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Proposed approach Supervised classification data mining algorithms:

Three classification algorithm i.e. Classification tree, Random Forest and Naïve Bayesian have been tested on the data. A main feature of our approach is to being rule based.

First we mine training data, then find rules and finally use them for prediction.

This rules can reveal some fact about traffic pattern

for example we can point out a period of time that traffic is heavy or finding effect of weather or design of road in the amount of traffic.

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Classification tree:

A tree will be made based on the training data that will be used to produce rules. Each rule of the tree is a path that starts from the root and ends in a leaf. When new data receive, a passing through a node of the tree will be accrued and the next state of traffic determined.

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Proposed approach Classification tree Each node of classification tree recognize from other nodes by one of below parameters Flow: • Density • Duration • Speed

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Random forest(Leo Brieman,2001):

builds a set of classification trees. Each tree is developed from a bootstrap sample from the training data. When developing individual trees, an arbitrary subset of attributes is drawn (hence the term "random") from which the best attribute for the split is selected. The classification is based on the majority vote from individually developed tree classifiers in the forest.

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Naïve Bayesian:

Use probability in determining an entrance data is in which group. Assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 4" in diameter. Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier considers all of these properties to independently contribute to the probability that this fruit is an apple.

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Results

Real-world traffic data set of Hakim highway in Tehran, Iran which has been gathered in the autumn 2011. Level of service with a definition corresponded to Highway Capacity Manual (Transportation Research Board, 2000) Orange software have been applied to mine the data. We test our method for 3 intervals 10, 15 and 30 minutes respectively.

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Results Due to lack of records in LOS B and E these levels have been merged with their adjacent ones.

◌Day ِ

30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

0%

LOS A LOS B LOS C LOS D LOS E LOS F

20% 40% 60% 80% 100% Fraction of day that road segment spent in specified LOS

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Results

Target class: A Examples: 12358 Prediction for 10 minutes Method

CA

Sens

Spec

AUC

Brier

Classification Tree

0.7887

0.9630

1.0000

0.8995

0.3591

Naïve Bayes

0.8345

0.9630

0.9922

0.9613

0.2492

Random Forest

0.7289

0.9259

0.9961

0.9556

0.3151

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Results


CA

Sens

Spec

AUC

Brier

Classification Tree

0.7923

0.7101

0.8583

0.8965

0.3626

Naïve Bayes

0.8254

0.8406

0.8333

0.9540

0.2828

Random Forest

0.7513

0.9420

0.6667

0.9537

0.2737

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Results


CA

Sens

Spec

AUC

Brier

Classification Tree

0.8404

1.000

1.000

0.9284

0.2770

Naïve Bayes

0.8511

0.8889

0.9882

0.9659

0.2560

Random Forest

0.8298

0.8889

0.9882

0.9597

0.2342

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Results Comparing to Classification Tree and Random Forest Naïve Bayes has the best Classification accuracy and Classification Tree after Naïve Bayes shows better accuracy proportionately. All three classification method performs better prediction as time intervals increase.

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Results One day Scatterplot [Speed (km/h) - Flow (veh/h)] X: Speed (km/h) Y: Flow (veh/h) Color: next State(Naive Bayes) Time Duration (15 min)

Real LOS

Prediction LOS

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Results Confusion Matrix Learner: Naive Bayes Data: Number of examples

A

C

D

F

A

14

2

0

0

16

C

3

32

2

0

37

D

0

5

26

1

32

F

0

0

1

8

9

17

39

29

9

94

Note: columns represent predictions, row represent true classes

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Results We just used data in previous days and found some simple rules then used them for the prediction. Clas s

P(Class)

P(Target)

# Inst

C

0.375

0.173

4121

Density (veh/km)